def get_model(args, n_dim, r, from_ckpt=False, train=True, grocery='false'):
"""Create a model based on arguments"""
if train:
opt_params = { 'alg' : args.alg, 'lr' : args.lr, 'b1' : 0.9, 'b2' : 0.999,
'batch_size': args.batch_size, 'layers': args.layers }
else:
opt_params = default_opt
print(args.model)
# create model
if args.model == 'audiounet':
model = models.AudioUNet(from_ckpt=from_ckpt, n_dim=n_dim, r=r,
opt_params=opt_params, log_prefix=args.logname)
elif args.model == 'audiotfilm':
model = models.AudioTfilm(from_ckpt=from_ckpt, n_dim=n_dim, r=r, pool_size = args.pool_size,
strides=args.strides, opt_params=opt_params, log_prefix=args.logname)
elif args.model == 'dnn':
model = models.DNN(from_ckpt=from_ckpt, n_dim=n_dim, r=r,
opt_params=opt_params, log_prefix=args.logname)
elif args.model == 'spline':
model = models.Spline(from_ckpt=from_ckpt, n_dim=n_dim, r=r,
opt_params=opt_params, log_prefix=args.logname)
else:
raise ValueError('Invalid model')
return model
def get_model(args, n_dim, r, from_ckpt=False, train=True):
"""Create a model based on arguments"""
if train:
opt_params = { 'alg' : args.alg, 'lr' : args.lr, 'b1' : 0.9, 'b2' : 0.999,
'batch_size': args.batch_size, 'layers': args.layers }
else:
opt_params = default_opt
# create model
model = models.AudioUNet(from_ckpt=from_ckpt, n_dim=n_dim, r=r,
opt_params=opt_params, log_prefix=args.logname)
return model
def get_model(args, n_dim, r, from_ckpt=False, train=True):
"""Create a model based on arguments"""
if train:
opt_params = {
"alg": args.alg,
"lr": args.lr,
"b1": 0.9,
"b2": 0.999,
"batch_size": args.batch_size,
"layers": args.layers,
}
else:
opt_params = default_opt
# create model
model = models.AudioUNet(
from_ckpt=from_ckpt,
n_dim=n_dim,
r=r,
opt_params=opt_params,
log_prefix=args.logname,
)
return model
